GGR438: SPATIAL DATA ANALYSIS

Fall 2005,  4 credit hours

prerequisites:   GGR 435/535 or GGR 436/536.

 

Instructor:           Ruihong Huang, Ph.D

Class meets:     MW 4:10-6:00pm in Lab034

Office:                Room 210, SWFSC (bldg 82)

 

Course Description

Spatial data analysis is a range of quantitative methods, usually implemented by GIS as spatial analysis tools, that are used for exploring and visualizing characteristics of spatial data , identifying spatial patterns and associations, and making prediction for unmeasured locations or future status.  Spatial analysis provides quantitative support for spatial decision making such as identifying the best location for new business establishments, modeling environment processes, maximizing the benefits of urban land use, as well as enhancing efficiency of transportation facilities.  This course focuses on vector-based spatial data analysis principles and techniques.  Contents include exploratory spatial data analysis, spatial modeling, and spatial statistics.

 

Student Learning Expectations and outcomes of the course

Students participating in this course are expected to have knowledge of basic statistics and have taken introductory GIS courses.  Upon completing the course students should have gained knowledge of spatial data analysis principles, enhanced comprehension of Geographic Information Science, and be able to perform

 Course structure/approach

The course will consist of lectures (including discussions) and labs each accounting for about 50% of the total time.  Principles will be illustrated by practical applications in lectures and enhanced by assigned literature reading and classroom discussions.  Spatial data analysis techniques will be trained in labs with the ArcGIS spatial analyst and GS+.

 

Textbook and Required Materials

O'Sullivan, D. and D.J. Unwin, 2003. Geographic Information Analysis, John Wiley and Sons, New Jersey.  ISBN 0471211761.

ESRI, 2003.  Using ArcGSI Geostatistical Analyst.  ESRI press.

 

 

Recommended materials/references

Paul Longley and Michael Batty (ed.), 1996, Spatial Analysis: Modelling in a GIS Environment, Pearson Professional Ltd., New York.

Goodchild, M.F., et al, 1996, GIS and environmental modeling: progress and research issues, GIS World Books, Fort Collins, Colorado.

 

Course Outline

Week 1:     Spatial data and geospatial data analysis

Week 2:     Vector-based GIS modeling and visualization

Week 3:     Descriptive spatial data statistics

Week 4:     Point pattern analysis 1:

                    Density-based, distance-based pattern measures, point-pattern statistics

Week 5:     Point pattern analysis 2:

                    Cluster detection, tesselations, thiessen (Voronio) polygons

Week 6:     Linear data analysis: networks, graphs and trees, shortest path

Week 7:     Polygon data analysis: spatial autocorrelation

Week 8:     Midterm review and exam

Week 9:     Deterministic spatial interpolations

Week 10:   Trend surface analysis

Week 11:   Semivariogram

Week 12:   Kriging 1: principles

Week 13:   Kriging 2: methods

Week 14:   Spatial regression (introduction)

Week 15:   Multivariate data analysis:

                    Distance, difference, similarity, cluster analysis

Week 16:   Final review and exam

 

Assessment of Student Learning Outcomes

Student performance will be evaluated based on lab assignments, exams, literature review notes, discussions, and attendance.

            Lab assignments:     300 points

            Midterm                     100 points

            Final exam                100 points

            Literature study          50 points

            Discussions               50 points

            Attendance                -10 points per absence

            Total:                         600 points

 

Grading System

A                     > 90%

B                      80-90%

C                     70-80%

D                     60-70%

F                      < 60%

 

Course Policies

 

Attendance is required for the course and will be monitored in lectures and labs.  10 points will be deducted from the total points a student earned for each absence.

 

INCOMPLETES: will not be given without written recommendation by the Dean of Students

 

PLAGIARISM:  I encourage a certain amount of collaboration among students. However, each student is required to complete individual assignments. Plagiarism of another student’s work or of material from other uncited sources will cause the student to fail the class.

 

Northern Arizona University Policy Statements

Safe environment policy, Students with disabilities, Institutional review board, and Academic integrity: http://jan.ucc.nau.edu/academicadmin/plcystmt.html